{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "collapsed": true,
        "pycharm": {
          "is_executing": false
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": "   total_bill   tip     sex smoker  day    time  size\n0       16.99  1.01  Female     No  Sun  Dinner     2\n1       10.34  1.66    Male     No  Sun  Dinner     3\n2       21.01  3.50    Male     No  Sun  Dinner     3\n3       23.68  3.31    Male     No  Sun  Dinner     2\n4       24.59  3.61  Female     No  Sun  Dinner     4",
            "text/html": "\u003cdiv\u003e\n\u003cstyle scoped\u003e\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n\u003c/style\u003e\n\u003ctable border\u003d\"1\" class\u003d\"dataframe\"\u003e\n  \u003cthead\u003e\n    \u003ctr style\u003d\"text-align: right;\"\u003e\n      \u003cth\u003e\u003c/th\u003e\n      \u003cth\u003etotal_bill\u003c/th\u003e\n      \u003cth\u003etip\u003c/th\u003e\n      \u003cth\u003esex\u003c/th\u003e\n      \u003cth\u003esmoker\u003c/th\u003e\n      \u003cth\u003eday\u003c/th\u003e\n      \u003cth\u003etime\u003c/th\u003e\n      \u003cth\u003esize\u003c/th\u003e\n    \u003c/tr\u003e\n  \u003c/thead\u003e\n  \u003ctbody\u003e\n    \u003ctr\u003e\n      \u003cth\u003e0\u003c/th\u003e\n      \u003ctd\u003e16.99\u003c/td\u003e\n      \u003ctd\u003e1.01\u003c/td\u003e\n      \u003ctd\u003eFemale\u003c/td\u003e\n      \u003ctd\u003eNo\u003c/td\u003e\n      \u003ctd\u003eSun\u003c/td\u003e\n      \u003ctd\u003eDinner\u003c/td\u003e\n      \u003ctd\u003e2\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e1\u003c/th\u003e\n      \u003ctd\u003e10.34\u003c/td\u003e\n      \u003ctd\u003e1.66\u003c/td\u003e\n      \u003ctd\u003eMale\u003c/td\u003e\n      \u003ctd\u003eNo\u003c/td\u003e\n      \u003ctd\u003eSun\u003c/td\u003e\n      \u003ctd\u003eDinner\u003c/td\u003e\n      \u003ctd\u003e3\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e2\u003c/th\u003e\n      \u003ctd\u003e21.01\u003c/td\u003e\n      \u003ctd\u003e3.50\u003c/td\u003e\n      \u003ctd\u003eMale\u003c/td\u003e\n      \u003ctd\u003eNo\u003c/td\u003e\n      \u003ctd\u003eSun\u003c/td\u003e\n      \u003ctd\u003eDinner\u003c/td\u003e\n      \u003ctd\u003e3\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e3\u003c/th\u003e\n      \u003ctd\u003e23.68\u003c/td\u003e\n      \u003ctd\u003e3.31\u003c/td\u003e\n      \u003ctd\u003eMale\u003c/td\u003e\n      \u003ctd\u003eNo\u003c/td\u003e\n      \u003ctd\u003eSun\u003c/td\u003e\n      \u003ctd\u003eDinner\u003c/td\u003e\n      \u003ctd\u003e2\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e4\u003c/th\u003e\n      \u003ctd\u003e24.59\u003c/td\u003e\n      \u003ctd\u003e3.61\u003c/td\u003e\n      \u003ctd\u003eFemale\u003c/td\u003e\n      \u003ctd\u003eNo\u003c/td\u003e\n      \u003ctd\u003eSun\u003c/td\u003e\n      \u003ctd\u003eDinner\u003c/td\u003e\n      \u003ctd\u003e4\u003c/td\u003e\n    \u003c/tr\u003e\n  \u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e"
          },
          "metadata": {},
          "output_type": "execute_result",
          "execution_count": 16
        }
      ],
      "source": "import numpy as np\nimport pandas as pd\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\n%matplotlib inline\n\nimport seaborn as sns\nsns.set(color_codes\u003dTrue)\n\nnp.random.seed(sum(map(ord, \"regression\")))\n\ntips \u003d sns.load_dataset(\"tips\")  # 导入内置数据集合, tips 消费集合, 需要翻墙\ntips.head()\n"
    },
    {
      "cell_type": "markdown",
      "source": "### 使用 regplot() 和 lmplot() 绘制线性回归关系\n* 二者均可\n* regplot 比较简单实用\n* mlplot 复杂但是功能多\n",
      "metadata": {
        "pycharm": {
          "metadata": false,
          "name": "#%% md\n"
        }
      }
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "outputs": [
        {
          "data": {
            "text/plain": "\u003cmatplotlib.axes._subplots.AxesSubplot at 0x214901f9ba8\u003e"
          },
          "metadata": {},
          "output_type": "execute_result",
          "execution_count": 12
        },
        {
          "data": {
            "text/plain": "\u003cFigure size 432x288 with 1 Axes\u003e",
            "image/png": 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95WIReFgtvJFlU7cumy6XCz6fz/j5rLPOgsVSn49IBEGUTqU2g1WzxuxcJGmkWBa1K+71Ym27q65SLHmegy0tnZKvk6ygRUV/27Zt+PSnP43rrrsOgiDg8ccfR0dHB37xi18AAN773vdWvZP1Cu2gXL3U4neb3sa6zma8aXN7zf5+hsf8+OEThxGOSZAVFZOBGI6dCeEv331OyX2oVo3ZaFzC8NgsBkf8ODYxn3Ntp03E1p429Pe2Y1OXu6rx73LgkMqySc3ml1vAvFosKvqJRAIdHR14+umnAQAOhwMOhwN79+4FYF7Rp6pFq5da/G6z25iYCeOhVMpmLf5+Hnv2GIKhBbsBWVYRDCXw2LPHirZfrR2z8aSMV0dmFrUr7u/z4uw0u+KVRhQ4YyZvXcGQTTksKvr33HNPLfrRcFDVotVLLX63ehvxhIxQTIKiMAgCh8eeO16Tv5/xPKGSYsdlJeWDU8Eds+l2xQdPBJCUcu2Kz1nfioE+L7Zs9NSFXbGeM29Lzejr5eZTDgVF/4YbbsD3v/99XHbZZRl3L8YYeJ7H73//+5p0sF6ppTWv2Sg1tFLJEEz6taYCMTQ5LLBnWeXqv9tKtDsdjCGekBFIzbY5joMsqxibmMfwmN+4XiltVaI/KmNQVQZZUbH7kWHjGpVemE23Kx4+6kc8mWVXDKB7bTPe0NeO87vbMD4dxosHp7BnaAIetw3bt3Tg7HWFa3AcGQ/ixYNTCIQSJZ1fChaBh81aWs58I4R8C4r+rl27AADnnXcebr/9djDGwHEcGGO47bbbatbBeqURt/o3AqWGVioZgsm+FoNmxuUBMoTf12qvWLu+VgeGx2ZzjosCbzxRlNLWUvuzzteEsQktnKQyBiW1Y9VqEXBmNoqf/mkUwXACfWctv8hRKXbFm7qacf4mD7b1etGcsis+Mh7Eb184aZzjn08YP+cT8nLPL0T6bL6cBdhGCfkWFP2vf/3rOHDgAKampnDgwAHjuKIo6OpaPbsEl0ojbvVvBEoNrVQyBJN9rSaHBcFQAqGYlCH6O/q7Ktbujv4uvDoyk3O8yWExnihKaWup/bnyrRvx4BOHEYpJiCdkcJyWO97ktBiWBfsOTC1L9CcDUSPFspBd8UBvuxan3+TNKRf44sGpvNd98eBUXhEv9/x0ypnNF6JRQr4FRf+b3/wmgsEgvvGNb+COO+5YeIMoFqxpayYacat/I1Bq2KyS4bXsa+kVkCIxCTzHZfxuf/5U7o1+Ke1u7faie40bJ6cjkBUVFpGH0y7CYRONp8VSxrjUz2Frtxcfe/c5eHrwNAZHZsALPJw2McOWNxDKFerFCITiGBr1F7Qrbm2yYiDlS7+mzVl04bNQ+5U4zqUsiMudzRejUUK+BUW/qakJTU1N+N73vlfL/jQUq2kHZb1QatiskuG1fNdy2ERsWuPGjddurVq7V751k/G0aBF5o+6q/rRYSltL7Y+sqNjQ6cb7L+lFPKnAn2cm7nGXtpM1FE1i+OhsQbtil8OCbT1tGOhtx4bO0u2KPW5bWf1a7HxR4AyRr8bmqEYJ+Tbe0jOxqikUHss+Xup5lWyz0u1u7fbiukt60OlxgOc5dHocuO6SnozqXou1VU5/GGOIJWTMzscxMxdHNK5ZJGzf0pH3GoWOA1qK5UuHpvC/f30A33zwZfzy2WMZgm+zCLjgHB/++v/bgls/dgGueVs3Nq5xlyW05fYr33GOAy4eWAtfqx3tLQ64nVZYLUJVUisr+bdRTchlk6grSg2bVTK8trXbi2NnQvjzK6cQiUlwOSx4xxvPynutSof19KdFn8+N6elQ2W2Vco6iqogllAwr43T0ePdiWS8l2RVv8GCgrzy74iPjQQw9dRST/khG26X2S+ecda0QBQ4vHZqGfz6BTk9ts2caJeTLMVbt6pNLJ/tLsNrJ98U3Eys1/uysC530WXe1qcbYE5K2iSrbYngx0tMeW5us6Gp3YSYYL2JX3IKB3nacu8kDu7W8eaSecSMKXMZN5PKL1peUcaOHbKwNtDkqH5X+/ft87oKv0UyfMD2NknVRCsu1Mj4yHsRv9p5AUlYRS8g4PRPB8Fgg57xNa9zo79OeUpocS/fiKjfjRuA5Q+Dryc+mkSDRJ0xPo2RdFEOSFUQTypKtjHW74l8+cwyBUCKvcdparxP9qcybStkVL5Zxk141ymrh69bPppEg0SdMT6NkXWSjzeoVxBJL98Hxz8UxODqDwZH8dsV6cQ+XXcTnr+tfbpdzyJdxw3FaDr+32V7XZQwbFRJ9wvQ0ykY7fYv/VCAKj9uON5zdviSLgflIEkOj2qapfF47PM/BYRXgsIlGaqO3uTqFSLZv6cAT+05C4DkwaDYMHMfhHW88iwS/SpDoE6anEbIuXjs6g5/9+ShUxsCYtsW/HIuBaFzG/jE/Bkf9GDuda1fssInY2t2G9hY7Xh2ZyVkQLZa+WS58amOU1SLgbdu64HHb8OKhGZycDNXlZ7/aINEnCNTvRjtFVRGNy/jDS6fyxtmLWQwkJQUHjgcwOOLHkfFgzvutIo9zN2kpln1ntRjx8jVeZ01Ny7Z2e3HpRZtMnblWS0j0CSKNenFJzE63nJ3Pv6icvRAqKyqOjM9hcGQGB44HjF2+Ohl2xRs8sFpyfWbSc+SXylJNy4jqQ6JPLEq9CGE1+zE85sdjzx7H2Jl5iAKPJocFao1dElWVIZayMs6elRezGFBVhrEz8xgc8WP/mB+xRJZdMQf0rG3GQK9mV+ywVedrn25zkO9mQtQHJPpEUerFLraa/dCvPR2MAWyhihSgxbqrna8vyQoC83FMB2MFyw5u39KRYRvMGIMkq1BUhnt/9HJeu+L1HU3o7/Vm2BVXEo4DrGJqNm9tzIIiZoREnyhKNTcuZc/cr3x7L9a3OWreD/3aclbaYzgmwWETq5KvzxjDy4ensee1Cfjn4uj0utDf01YwrKIf3zM0gTOzUSSSCpKyipm5zL6l2xV7myufciryHKzWxioPSGRCok8UpVobl/LN3B/49eu45m2b8op4NTdQ6dcWBR5yWgxcvwlUMl9fVrSF2cHRmYyZ+3Qgit++oKVPZgt/IJTA0OgMhkb9mPAvz664XPRi3zYLD2sdF/smSodEnyhKtTYulTtzr+YGKv3aboclY2FUF7jl5uvrxcRjiYWyg4vZD4RjEl476sfQiB/HJ3OzWnS74jf0tWN9R+l2xaVgzOZTu2BpNr+6INEnilLOxqVyFloLzdxPTIax+5FhHJ8MQZK14iIbO91Y19GUV/QrsYFKH6PdJsIDIBSTICsq1vtcuPKt+Z88SqGYu2U++wFVZRifDuN///oARk/N5RQgt1kEnN/dhoE+L3rWtkCoUEZMutUBxeZXPyT6K0Q+gby0iDNeNdstJmrpG5eOT4YRjUmQFBXff2Q/1qWJYna45tiZEIbHZmG18OA5zhBvvb18M/doXEYgFEcglEBSUsBxAM9xxmakCzf7MD4VLrqBaikZPtmbs1qbrAA4JGXFeCLJ185jzx4zdrSu8zXhyrduxNZur5FumZSUgguzejaO/hQQT8pG1k0glDTOEwUOWzZ6MNBbnl1xMThoRVtsVk3oK7nztV4yvYjCCHfdddddtW7U7/fjPe95Dy6++GK0tbUVPC8aTRZ8rZHRBTISl8EAROIyDhwPYE27Cy3LcCxcarvtLXZ0eJwF39fhccJuE/DaqB/hmATGtFlpMJzE4fE5rPE6sWdoApG4DACIJWQEQwnIsop4UoGsaB4xCUnBkfE5tLfYsaHTjQPHM90bZ4IxKCqDkragypgWB29yWqGqDDdeuxWXvGEttm/pyOnzUsenj3H7lg54W2x47egsZJUVvMbwmB8/fOIwzvijUFUGVWUIhOI4fCIIp0MreZhvI5WOojLMzsdx6OQcguEEYgklw1aY54Cz17fgsgvW4f2X9OCNZ/vQ4XEsa2bP8xzsVhFNDhHNLiucdgusolCxpwVgeZ+/y2Vbtd/3Uqj0+F2uwrYZNZ/pS5KEr371q7Db69vMqpoUimf/4YUT+MQVm2vebikZMHuGJhCK5aYFhmOSMbNLPwZohmDp0qcXGt8zNGGUIUy3PpgMRMFzHLLnx/oGIz30U2gWWYkMn1KLketjTC9HEYpJeOH1SfSubcl5v8oYTk6GMTgyg9eO+o0bZDqdHgfefF4ntvYsz64YWJjNW9PKA1ab1WRRvZqpuejfe++9+PCHP4z77rtv0XM9HifEJVamr2cC4WTeL+GZ2UjR4gfVajcYSS7abiCchKKwnEU9RWEIRpJY19mMiZmwcYzjdAMtzniPojJYRN5o71KfG5detMm41kfu/DUkVQUHDoxLF34OkqwiHJMwG0pAEHjMhhJ49JljaGlx4oLNHcseX/o4F7vGbOopRuvawuehMIZQTEJbmwuAdkMYnwpj3+uTePHAZN5dtes73XjTeZ3YvqUTbS3LmwjxHAe7VYDNKsBuFWu+C3a5n381//YbgVqNv6ai//DDD6OtrQ1vf/vbSxL9QCA3PW014Gmy5l2U3Limuar+I4Xa7fQ4Fm3X02TFKYHLSGkEAFHk0eqy4k2b2/HQmXkAgJA6jwPA8wuzYVHQin8Xam/TmmYcPhEAzwNK2qZSi4VHMJxAk8OSYyvw2NOjRm7/csaXPs5C15g4M4doQobTptkKKEq2xQEPt8OCI2P+lF2xP++CtbfFjoFeL/r72tHRqvW9rcWO2dlcx8vFSPe0EUUBckKFnJBQ/pWWz3I+f6oaV7vKWTVdpn/ooYfw7LPPYufOnThw4ABuueUWTE9P17ILdUGhjJN3XrRhRdotJQNmR38X3HlCDk0OixFm0Yt8Wy0CZIVBZYCsMEiKCpUx4/2F2vvAu86Bx22D1SJAEHjwPAdB4LFpTRPcTkte+4D0PP1KFKbOPpcxLWY/0Oc1iolfuLkDTruYcx7PARP+KP7///Mqfv/ieIbgN7us2LGtCze9byv+7oMDeNf29YbglwPHAXargBaXFR2tDnhb7GhyWHJMzFaCRikMbnZqOtN/8MEHjX/v3LkTd911F3w+Xy27UBcUsvK9YHNHVWc7S7UQ1jMywAE2qwBJViHwXEb2Tvr1j58JgecBqIDKtMVYxoBWtw1XvmVjwfYu2NyBj737nLz92/3I8KJ5+ksZX75sk+su6cFTg6cxNRtDq9uKCzd3oLtrIU5/9rpWXLujG3948SQm/FEoauoGl9CqV+nodsUDfe3Y1OUGv8R890bxtGkEi2qCUjZXjJWy8i233fRUTLtVNApfFyoari/48hwHXlgQOVHk0eKylpQ+me+cUvcLlDO+7DTTM7NR/N8/jeKKN6/H+y/uzfuedLviUzPRnCwdi8jjvDx2xeVQC0+baqVW1qtFNbHAion+Aw88sFJNE2VQbkbGdDCW42EDaGmXy7FMqMYsUh8bYwwqY1BT3X5+/2TGzL5Uu+L+Xi/O3ZjfrngxRIGH0y7WxNOmXkz0iJWBZvpEUcr1vNE3XeUs+Ar8si0TljOLzJ7ZnuVzYfioH5KiQuB5uOwibKmnmEAoYdgVD434MZzPrhhA99pmDPS14/xNbTkx/sXI9rTpbHOCV5RF31cJKLXS3JDoE0Up1/NmR38Xjp8J5dgM6Au+K0H6zJYxhrHT83jl8LQWR2GAoqiYjyThZgw8z4OhsF3xOp8LA33tS7Irtgg8rBYe1hX2tJkOxhBPyIbdhChoWUfVcBMl6g8S/VVMJeK2+WLpsYSMuXASt933nGY3IKsQec6wInhbfxd+t+8kIjEJHMehy+vABy8722i7lH7pRU3Gp7Xcf4/bCpfdiqSsZLznV88dw59fOYVwTEKTw4J3vPEsXPWWTRnXenrwtLZzNlVfNhhJQFYYGJhWiBva/wRCyby2Cb5WBwb6vBjobYe3jFx6UeC02bwowJKyo6gHrKKAU6GFpE5ZVhEIJdDqrk7x80bATPYRJPqrlErFbbNj6VaRQzwBBMIJzM7FjYVMReAwNjGP+x87AJtFQFuzHW15/NyL9Uv3Hhoe8+PBJw4bTwsqYzg1HYXAx+BtsRvv2fv6JPYdWHCrDEcl/OqZYwCAq96yCbKiIpaQMeGPGuZl8aQMSV6Qdpb6L1vtW5tEoW4FAAAgAElEQVSs6O9tx0Bf6XbFjVNUpIBFBCtsHbGaMdsaB4l+A1HObKSScVs9lj485sd/PX4Q4ZgERdFmzjqqysALHEJRCUmrCntWTr3ebrF+6btzsy0fdIdKNbXjVb92uuDrMMbwp5fH8batXUZ9Wd3cTFEZ5iOF/U0EnsObtnRgoK8d6zubcmbmR8aDOQXDN2/wwG6kUzaGDXFSVtHqtiGcFt5pcliQlM0p+mZb4yDRbxDKnY1UuuiI3n44JgEpwzVdIjgszB1VxvJm7+jtltKv45MhxBNyysZBy/XnsGC+piMpKiyplMh0D5xwTDIEP56U0dJkw+GTc8axbDQ3T+Av330ONm/w5D3nyHjQKHrCcZrlwB9eGofHbWs4YfC1OqAGYjmb3SpZLKaRqGaBnnqkXp8/iSyKzUby4Suw23OpX2y9HT3vPHtCq//Ic1ze3HS93cX6NTzmX1hAZQsRB5ZqM/3aIs+BMZYh+ADgsAoYPurHg787jG/8z0vYMzSRI/iioNk9i4LmU7+h011Q8DkOeOXwNASegyho4xN4zVOo0Odfz9DO2Uwq/V2pd0j0G4RyZyOV/mLr7etWCtmhD47jICsqGLSC3Wf8UUz4I0amiN7uYv3aMzSBJoclr1kYz3FwOyxgjEFRVWzrXZhhKyqDpGj/haIyfvT7I9g/NpuxecpmEeCyi/C4rejwOOFrdaDD40Jbsx3veONZGW1ZBC2Ns81tQ0erA3ORJHieywnfNOJsMN0yg+c4dHocBTfbmQGz3QQpvNMglJs6WcpmpnLWCKwij5PTEciKCo7jIAgw4jpcynzMIgqw2wREohIkpmrCLWQuGxbqFwD88wMv4tWRGYgCD5fDgnhCgSQr4KDF2zd1NSOeVNDismL7lg70nqVVj3rlyExGlan09qwiD4dNhN0mGt7xNpFDk9OaGZtf32rYENssQs5Np5rlGlcC2jm7gNnsI0j0G4RyyhbqFPtiL7ZGkH5DsIp8xoYrPZrf1mzHX6a8cnRBnA7GwPMceHAQRd54dE5fFEtfGN4zNIH7f30AoYgEnue01EpFgZxabLRbBagM8Lpt+NA7zwZjDBP+KIZGZ/Dzp44iGM5dmBUFHg6bgKSkwNuS++guKQwf+4tzjBKBVgu/qF3CUj7/amCm1MJaYqabIIl+g1Dp2chiawTpAndyOgJZVuG0i0jKqpHxoS9i/vyphXPTF1rT/50dBtFvOrGEjPlwUqvGxZhWREXV4vShaBKioM2k+9a14I8vjxe0KxZ4Dg6bVrVK93QPZPnXcwA4HljjcS5aySkb/XP+6R9HMOGPgjEGl8OCY2dCNROLcm7UdEMgCkGi30BU0obg+GTIME9LZzoYz7kh6OKdlFX4Wh3Gbs5jE/PY/cgwrCKPuKTlxOupnFrlpgUPmuwwiN5GOCYZ4RhD+Hnt35KsgDFtjSDfLLvZacG2Xi/OzEYRjcs58fY1XieSsgqe48BxMF6/+A1ry/rsdI6dCWEqEEuFiTgkkkrGvoClMjzmx77fHML45HxRsS7nRr3ac82JpUOibwL0mq56XvZkIAZFUdHSpL0+F05AklUwps2Exya0YigcUk4FTPt/lTHEE7LmTZPa3frqyAyYipRh2UIaJwOQkBScOKNZRU8ForjtvuewsdONdR1NGBz1Q5IUZJeSZQD0BwRV0fzp07FaeAykNk1tWtMMnudwZDyIR/eMIRKXoagLeecfuqzXyLCpxNPRn185VfC4LvqFZtvFjj/05FFYRB4qKy7WxRbz6yHXPN8Y9T6U8vRBTyq1gUTfBDz27DEE07xwZFkrahKYT6QyYdLEOi3FniEtZZIBUBmCYU3wFYVB4DmoysL7821L0q+rLcrGkUgqePnwdEYh8FLQc+lddhFbe9rQk1aHVk+f1Gfz6f9VMlYbzlMjGAAiqeOFwi/HzoTw0qHpnONAeRuDii0mr3Sueb6x//CJw+AAYzNdsRvay4em6EmlRlDKpgkYn84tnsdzHBS9cHkJm0g5DrBYBOOJQOA5beE1LUe+kPDrKIq2I1YpU/BFXsvJ5zkOsYSClw5NZVSPGhr1w+WwoMPjRJfXBV+rA45UAfZ0hsf82P3IMO7+733Y/cgwhsf8ZfWjULFyV+p4IQEv9ISQXVA+nXxiXSy1cKVzzfONPRyTMnZWFzv39y+cKPm6xPKgmb6ZYQyCyENgWuHxYlIs8BzWtDkxE4xBUlRjCl+qXUt63L4YPKdVnEpIMuTUfqr0WL2iqghFZYxPhw3RnArE4HLkllNMF85K+Ku8441nGTH87ONae/kFPBKT4M7jyKmFnEpPBV1sMX8ls4vyjT3fzmzt3Nwb2hl//qq+jbgPot4h0TcB63xNRpw+HZfDAkVlWhFzrrAg8xyMwiDrOppwcipspG/q7xN4LqeKVKnoNg48pxVVV1QGnuPBYUE0dOEXRR5WkcsQOAYY4at04U8XzkrEvPW4/Z9fOYVITIIry9WzkIC7Cjwh6KJdjlgXCletdK55vrEXSoPNd0Nb43XhxJncv9FG3QdRz5Dom4Ar37oRDz5xOMc//W39XdgzNIFgKAGeywzVpMNznBHauPItG3HsTAi/2XsCsqLCIvIpkeZgs/KIxUsvBMKn4vSMASqgbYhK+dtrWTxcajF5Yaav9SMziOR2WBAIJRCOSRminy6clYp5X/WWTQUzdQoJ+DveeFZGTD/9fF2UXzw0g5OToWWJ9Urmmucbe5PDkjfcl++G9q6LNuD+R4dLOpdYHiT6qxDNi/6YEctf52vC2/q7MD4VzpkFblrjNs4VFG0XraxomTg8z8FuFeC0W7Cxs8l4T/r7Tk6FwRiDReSNHa2RmOaxnw3PAd5mO3rXNeOMP4rJ2RjAAQLHQRA4JCQVyZRHjkUU4Gu1w+WwYHwqnBqHVog9fV8AoC0UeqDFkHmOyyuctdhRW2y2vWmNu+AsfGu3F5detAnT06GK9aXWFNtpXcrTxwWbOzB3SY9pdsWuJBzLdquqIxr5S7AUfD73ssesp2cGsypXedw2fOzd51TkS8QYwytHpvGTP4wgFE1CUrTF3Xx/SS67iG09Xgz0tWNDZxMEgc+xIr7tvucQmE+A47gM87S2Zjv+6TP/K+eaux8ZzivgnR4Hbrx2a94+Z8f0derFc6YSv/tGhsZf2fH7UrUp8kEz/VXGnqGJvKmFoZi07JzthKQgnpARjCTwkz+MwD8Xz7v4axH5lNB70bO2BRaRh80iwG7VxD6b7ELjOkk5f6hoqZYUgHn8VQiiECT6dcTLh6bw2NOjy9qcMh2MQU7FxNW0/HmVLS0TIikpiEsKQlEJB44HMDQyg4MnAkXz7Ntb7Lhwczu29bTDZhEMW4RCFHo9fUdvOksVcDP5qxBEIUj064ThMT8efeaYMetd6uYUX6sDp2eiUNLS5RgAVVVhtSy+LYMxhoSkICGpiMYljJyaw+CIH/vHZgsWIcnGIvIYHPHjLeeXtgi3sdMNxpCqt6sYO2o3djYVfE+5Al7r3Z60u5SoV0j064TFfFWKCcivnjtmFCIHxxnlBdPhOQ6RmIR7H3zZKDauL4yet7EN8aSiiX1SxvHJMAZHZvDa2Kyx2zQdm4WHVeQRics5NgocgERSwYnJMHY/MlyS6O3o78JkIIZmlzUj1FOpzI1a10A1W83VRoFuxBrCXXfddddKd6IQ0Wjheqarjcf3HjeshdPxzydwZHwOkbhWPjASl3HgeADtLXZ0eJz41XPH8OjTY0gkU7PwPFEXTvMHQygmYT6ShKoyKIqKQCiBgycCcNgEhGISnnz1NB5+6iieee0MxqcjBWPtqsogCFzKIC3zNZHnkFRUY7NXvj5n0+Fxor3FjlBMRiiShC1ldbx/bBYHjgdgtwllu2Km8/BTRxGJyznHZ+fj2L6lY8nXrWR7LpfNVH/v2VR7/PqNuND3aKWp9PhdLlvB12imXyf4Wh2Yzcq4ATSnSbs1N7atL8r++ZVTRfLrFzbIaHF+gHFptgmMYS6cxI9/P5I3dONttkPggUAoASkVw9fzruNJBYLAw2bhkZSUlDeOZs0gSQpa8tgCFFtI1tMW//TCsYrPkmvtS7PSPjhELvVgSFcvkPdOnVAolFFokVMXkHBMKriTVmUwasjqM3LdYE1WGGQVkBWWIfjNTgvetm0NPve+rfjHnRfAYRehqJpVcvpGG5Yqjr7G64TP49BMtTikvPC1fsUTmbPdUkSv3FrApVBrX5qV9sEhcqEb8QI0068TtnZ70dLiTGXvLGSkpFelAoBYQkY4JoGDlq9uEwXIKRM0IDO6k7J9B89xEJgmyPkiNgLP4YJzfBjo82Lzeg+cdhF2qwCO49DpceLkZDgnasRxCzckh00EByAgqwCnFR2XZS185JQUo/BKk8OC4TF/0ZlVNb6cta56VS9VtogFVlu5y+VAol9HXLC5A+vbcmeJuoDEErKx6crjtmEyENNi68gN5XMAnHYRssIQT+bPuuFSVsUfuLQPF57jg90q5tSG3dHfpdWgzbpbMAa0t9hwXWoX5f6xWYgib9g1BEMJqKrmqqmHmCwCv2iophpfzlrn6NOegPqDbsQLkOjXOekCogur22GBzSogEpMQlxTN115dCOHouh2OZYZXeA6wWzV7ZIHn4Gm2w+0Q8edXTmH/2GyGMKVbOchZgq8XKnc5rEbq5N3/vS9nUVffvKX3WfdVLxZH3dHflVHwRU/fXO6Xs9Y5+rQnoL6gG/ECJPoNgC4gX/+vF6CoWlw+HJMQiiSNeH263qaLLwege20zBnq92NrjRZvbBodNxOHxIB568igSsnZy+oIpgLxWDhwHCDwPq0UT8aS0cDPInqE7bCIEgYNNEHJi3IuFarJNukqw+wdAKXlEcehGrEGiX8eojCGRVJCUtBz6ZqcV/lS1q3BUgpI2u8/GIvJob7bj4+/ZAl+LHQ6bCJtVAJ9yrFxswTTdyoFLeR9z0MoV6iKeHnLJ9/isu3lmUyxUs2doAnabaDwVpB8v9oWl3HiCKA0S/TpDVjSnyXhSySls0reuBWP7xrUC5AXUnueB9hYHRIGHKHDoXduc19c8fcFUL3QuKypmgjE0OS0ZBTDSq2ylH08PueR7fL5ws6+gpXAhlrqQSyl5BFEaJPp1gCRrIs9mo5iZyxS3YDiBoRE/BkdncoqEA1qcXnenVJlW4zYUTaLZacU6n7tIIQstHKMXOtfRhD+u5f7n2W1rEXh0evKHTvI9PhezFE5neMyPfb85hKlADAzIWAPQ+lt8IbfUm0UtQ0AUbiLqkZqKviRJuP3223Hq1Ckkk0l89rOfxTvf+c5adqEqLPblzn79bdvW4Ox1rUikrA8OnQzixYNTODkVRiQmQVKYsRibb0Iv8Bza3DbMhhNavdmsOrWxhIJYIobJQAwvHPgjRIFLXYtB5HlYLDxUVcvq0d+qV6/SN1khV/PBmLYpay6cf+dgoc+hkNDp5x+fDCEUldDaZIXLYUEwlEAglIAHC0W1F1vItYo8Tk5HMhZ/HTbRuFloC9PHMXZmHqKg2UhMBmJ4dWQG3WvcuPKtmyrq5VNOuEm/4R0+MQtJ1grTbOx0002CqAo1Ff1HH30Ura2t+Jd/+RcEAgG8733va3jRX+zL/avnjuE3e09AShmJzUcljE9HcPlF63H2ulYcGQ/ity+cxHwkkZFtk0/sOU6rRmQROMzOx6FiQayLke6ImVTVvAVOWNo/VL1geh4UlWH01BwefOJwhj9/uTH19PNDUQmyrMI/l0BLkxWtbhvCMQnhmISNaxYXv+ExP4LhpJFlJMuqsQi9o7/LaGs6GAOY5hwai8tGcfeT05Gy4v+ljLXUcJN+LUlWEZhfeOJiDMbCOAk/UUlquiP3iiuuwBe/+EXjZ0HIb53bSBT6cj89eBqvHJnG488fhyQpANPEaD6cQDwp48WDU5BkFX94aRyz8/Gc9Mp8iLxWwDyWVKCmFlYXsy0uRqlZMdmojBn+/Drl7qRNP56+TqCXPPS1OtDhceLGa7cuKnr64q/HbYMo8toGMZGHx23D1m6v0Zbeju5vpNtX6MdL3fVbyphKDTfp75mPZD496Qvpy9mJTBD5qOlM3+VyAQDC4TC+8IUv4Oabby56vsfjhFjAU71eCISThvAacXXGMD0Xx7P7J7VZdlqNV8YYIjEJRyfmcc+DLyGeKL2mLMelNmKlpvgMmfVjyybfY4J+uSKPDwzajD8YSRoVetI/h3TSz0kn/XyrKBjmborCjONr25uKVgDKvpZFtMLtsmb00+dzG6/r7bDUOPXPT3svX7CvxfpeaKzrOpsxMRPOOSd7TPq1JFnN+F3qn0OpfVoNmGWchajV+Gu+kDsxMYGbbroJH/3oR3H11VcXPTcQyF24rCcYY2h2WjAZiOaUC/Q22zDpj0DgOciyool1ypVS87vJ72CZD604uPZvIRXs51IPBopa+nVyB5CnHcCok1usPwLPodVlNUq8eZqsBUsY5isDl36+yy4iENLKJQoCZ9wAtm9uL6mE3GJt668b7UD7XXCc9jt02kVIslqwr+W2BwBv2tyOh87M55yTPSb9WhaRN+oDA9qTSjl9anSoXGLtyiXWNLwzMzODT37yk/jyl7+M66+/vpZNVwxZURGJSwiEEpgKxNDf64Wq5taHvXCzz3DHlFVAUXPj9G1u26LhGZ7XRJgxQFJUKAqDVeTBcxw4jtMWcpeBwHPgoIWOfB4H2lsdsFoEw04hb584Du6sXbI7+rsQS8iYDsYw4Y9gOhhDLCEXXIBNP66HZiwiD7fTik6Po6zatYu1of+/0Y5FAFLeQa2pzWrFrlNue4AWh7/ukh50ehzgOa7gmPT3NKc9oQAwPn8z2gQQ1aWmM/3du3djfn4e3/3ud/Hd734XAPCDH/wAdnv9mh6pjEGS1FQ1KSUnP/7sda0AgBcPTmkGY3YRLocFv37+BKbyzAY5AJvWuvGGvnbsfX0KoVgSAoecXbWAZplgEXnEEnKq6hUHBoakrOLN53diaNSPSMp8TRB48ByQTDNfy2iX09oWBR5WiwCOw4Idg9sGl11EUmYZaZV6cRbdyZPjtCyZTV3NuPItG3MErJydtNl5/RvXuHHl23vzeg8txmJb7PO1ta6jCeNT4SVtyS91S38pO0D11188NANVZUjKCiyigI2dTZS9Q1QFjrFCxrwrz0o97kmyJvJJKXeDVD7mo0m8NurH4MgMxqcjOa+LAge7VUCnx4m3D3ThnPUe/Pj3h+GfTyAQimsumcjMsuEAtDRZ0elxIp7ldR9LyJAVFeGYlJGeqMNzHN53cXdeg6liM+jl5JXvfmS4YMjjxmu3lnQNMz/im3nsAI2/luEd2pwFbTafTNWFTUhK3nKD2cQSMvaPzeLVkRmMnZ7PuTE4bALO7/ZioM+L7jXNGe6VFoFPuU9yaHZaEQglckzNGIBgOIm5SBK+Voch6obTZqpASnp6on6Or9Ve9g7V5doYkF85QTQGphX9cmfzAJCUFRw8HsDgiB+HTwZzQj0Wkce5Gz0Y6GvH2etaMnbDchzgsIpw2ERYRB6dbU5jZqxn5eSDMWSIup7Kp/va6Ltp9VRHQIsD//ypo4b3fvqGpUIivGdoIsOOQb9+qTYG5FdOEI2BaUSfMYakpCKeis2XMpsHtOyYI+NzGBrx4/VjszkbmwSew9nrWjHQ58W5Gz2wWrTF2yPj2i7bYDgBX6sDl7xhLbb1tBvv29HfhQefOJxhgVAIKeWJYxF5rX2mPwkkoKbSRKWEjJlgDOt8WlqsVeRxKt1eIfVE4Gmy5m3j+GTI6IuqMsRlGbG4jLlIctHCJ/p4FvMrb1RbgkbtN0HkY1WLvqykFmDzmJcVQ2UMxybmMTjix/DYLGJZZf/S7YrP7/bi1EwYf355HI/uGQOgxeLllJVCOC5jMhDDwRNBXPHmDbjqLZuM60RTsfnF0G1w9EVanoMRDuJ4LW9f360bl1Q89ORRKIWuWyCvX0+TVFWW8QQjyWpJYZ7FFjcb1QWzUftNEIVYVaKvz+YLZdos9t7TMxEMjvgxdNSfs0MSANb5XOjvbce2Xi9aUil2R8aDeGTPGEJp50/OxsCnUiH1WL4sq/jN3hPYtEZbYHnoyaNISIoRAlKyxDYfAscBvLbgq0u3qqZy6zkOoZhkeNUEQgl43LaccE2ywP4APXU0u8g6l2bFXEomSqFzGtUFs1H7TRCFaHjRX+psXmcqGMPQyAwGR/3wz+XGu32tDgz0eTHQ2w5vS258+qVDU8aTgLGjktMEnOcAPi1xUVbUDBHRF2IBLUwkCFxBUQYWDNM4MKQ80QAGw0Mm+6khny99oRj7xk43GANmgjGAW9ikpaWKLn1BVg+NvDoykzfTqN4XemmBmlhtNJzop2faJMuczesEwwkMjfoxNDKD03nsilubrOjvbcdAnxdr2px5rQ5EnoPTLmI+koSisoxzdHeD7GRYUeBTYqG9YBF5xBOyZgcALdfeaReRSCqGpUM6+vV4njN25ioqM54m0heO1/lciOe5gRTbWDQZiMFuEzMyifQiKEtZkE0PjRTLNKpnaIGaWG00hOgvJdMmm3BMwvCYH0Mjfhw7k5sP67KL2NrjxRv62rG+s8moMJUOB8BmFeC0icaCbYfHialgPEMoeZ6DqmiOaLKiGqLusC9Y/R47E0IsLmsFSlQGFYAqq3BYBcRTLpeaTcDCrFvfUuF2WhCNa08XTU4LYql/p1epuvKtmwCUXhNUP/7Ys8cwdiZkhINKtTbOR/pTTbFMo3qGCmoTq426Fv1IXEIkJhUsCbgYiaSC14/NYnB0BiPjcznXsVkEnN/tQX9vO3rPajFmz9kIPAeHTYTTJmbk2wPal//YmVBGPVme42C38tqMHQse9bG4jHUdTdi0xo3hsdmM6+jCHo5JWgpnykaTS+XjcxzQ0eqAy2FFUlLR2cYDjCEpM1g92olJSS24G7UU9Jj8QrbK8gpIp4dG7DYRHgChmARFUQsWYqk3qKA2sdqoa9GXZbVswZdkFYdOBjE0MoODJwI5xmGiwGHzBg8Ger3YvMFT1PvGZtFm9TZrYafPrd1e/OW7z8Fjzx4zduOu62gCGEMwnMxZSB2fCuOqt2yC22lBKCohnpC1mwLPaR4+0EJHosBDELQ4fZPDgk+8Z0vNhKZSBaSzQyP6GkM5u3TrgUYoqE1ppUSp1LXol4qiMhw9PYfBET/2j80ikWVbwHNA71ktGOhrx3mbPLBbCw+b5zRxctrEgqUGs8knCnf/9768C6n6AuDGTjcmA5o5mR6vUqCmORsz+FqdqT5xDfkFptBIbaC0UqIcGlb0GWM4ORXGqyMzeO3oLCKpnarpbOhswkBvO7b2tMHtzL8pSceSWkS1W4XledSnWGwBUBfE9AweDhz41H0m/YZT6qJhvc32KDRSGyitlCiHhhP9M7NRDI7MYGjUn3c3a5fXif5eL/p72+Fx24peK9saoZIsNsvNt3Da5BQRjmr5/unWxqXMjOt1ttcIoZFGh9JKiXJoCNGfnY9jaNSPV0dm8toVtzXbMNDbjv4+Lzo9ThwZD+I3e48bG5S2b+kwLJABLa7vtFngsBWe1Zcyay52Timz3OyF02Akqc3qUwu0pc6Mh8f8+K/HD+Z13azGbK/enijMDqWVEuVQ16L/1OBpvHhoGiencsvOuR0W9Pd6MdDXjrN8LkO89ULjOv75hPHzth5vRrplIUqZNZdyTqmzXP28pdir6v0IxySjDm96LnylZ3v1+kRhZmjthCiHuhb9nz89lvGz3Spga48XA71edHc156RPAloxk2x4nsPwUT/e3r+2pHZLiZHWSxxV70f62gCQXmC8srO9ehk3sQCtnRDlUNeiDxS3K86HHufXc+P1G8PM3OJuljqlxEjrJY6q9yN98xMAw5Kh0rO9ehk3kQmtnRClUtei/8XrtqG12Q7bIuEYHZ7n4Gt1wD8fz4nVlzPjLSVGWi9xVL0f6Zuf9Nz+curMltte7nGKHxNEI1DTwujlsqmruSTBt4o8Wpus6Gh14NILzsq7OFvOjLeUwtelnFMLsguM+1od6PK6qraZq17GTRDE0qjrmX4xeE5bqHRkbaKqRHyz1Myb5bZTCWrdj3oZN0EQS6OuC6OPjM0glszcXWsVeThsldtEVU9QcWjzjt/MYwdo/FQYPQt9E5XTXro1AkEQBJFLXYu+IPBwO7WZfT6rY4IgCKI86lr0060ICIIgiOVDsRKCIAgTQaJPEARhIkj0CYIgTASJPkEQhIkg0ScIgjARJPoEQRAmgkSfIAjCRJDoEwRBmAgSfYIgCBNR14ZrBEEQRGWhmT5BEISJINEnCIIwEST6BEEQJoJEnyAIwkSQ6BMEQZgIEn2CIAgTQaJPEARhIuq6ctZqZ3BwEP/6r/+KBx54AMePH8ett94KjuNw9tln42tf+xp4fnXekyVJwu23345Tp04hmUzis5/9LPr6+kwzfkVRcMcdd2BsbAyCIOCee+4BY8w049fx+/14//vfj/vvvx+iKJpq/O9973vhdmvFy9etW4cPfehD+MY3vgFBELBjxw58/vOfr17jjFgR7rvvPnbVVVexD3zgA4wxxm644Qb2/PPPM8YYu/POO9kTTzyxkt2rKj/72c/Yrl27GGOMzc7OsksuucRU4//d737Hbr31VsYYY88//zy78cYbTTV+xhhLJpPsc5/7HHv3u9/NRkZGTDX+eDzOrr322oxj11xzDTt+/DhTVZV96lOfYsPDw1Vrf/XeSuucDRs24Nvf/rbx8/79+3HRRRcBAC6++GI8++yzK9W1qnPFFVfgi1/8ovGzIAimGv+73vUu3H333QCA06dPo7293VTjB4B7770XH/7wh9HR0QHAXH//Bw8eRCwWwyc/+Ul8/OMfx759+5BMJrFhwwZwHIcdO3bgueeeq1r7JPorxOWXXw5RXIiuMcbAcWllw0UAAASnSURBVBwAwOVyIRQKrVTXqo7L5UJTUxPC4TC+8IUv4OabbzbV+AFAFEXccsstuPvuu3H55ZebavwPP/ww2tra8Pa3v904Zqbx2+12/M3f/A3+8z//E1//+tdx2223weFwGK9Xe/wk+nVCevwyEomgubl5BXtTfSYmJvDxj38c1157La6++mrTjR/QZru//e1vceeddyKRSBjHV/v4H3roITz77LPYuXMnDhw4gFtuuQWzs7PG66t9/N3d3bjmmmvAcRy6u7vhdrsRDAaN16s9fhL9OuG8887D3r17AQBPPfUUtm/fvsI9qh4zMzP45Cc/iS9/+cu4/vrrAZhr/L/4xS/w/e9/HwDgcDjAcRy2bt1qmvE/+OCD+OEPf4gHHngA5557Lu69915cfPHFphn/z372M3zzm98EAExOTiIWi8HpdOLEiRNgjGHPnj1VHT+5bK4g4+Pj+Lu/+zv89Kc/xdjYGO68805IkoSenh7s2rULgiCsdBerwq5du/D444+jp6fHOPaP//iP2LVrlynGH41Gcdttt2FmZgayLOPTn/40ent7TfP7T2fnzp246667wPO8acafTCZx22234fTp0+A4Dv/wD/8AnufxT//0T1AUBTt27MCXvvSlqrVPok8QBGEiKLxDEARhIkj0CYIgTASJPkEQhIkg0ScIgjARJPoEQRAmgkSfIAjCRJDoE6YhFArhpptuKnrObbfdhlOnThU9Z+fOncZGonyMj4/jsssuy/vapz/9aUxOTuLhhx/GrbfeCgC47LLLMD4+vkjvCaIykOgTpmFubg4HDhwoes7evXtRza0rP/jBD9DZ2Vm16xPEYpDoE6Zh165dmJqawk033YSHHnoIV111Fa6++mrceuutiEQiuO+++zA1NYXPfOYzCAQCePzxx/HBD34Q11xzDa644gq8/PLLJbeVSCTwxS9+Eddccw0+//nPY25uDgDN6omVh0SfMA133HEHOjo68IUvfAG7d+/GAw88gF/+8pdwOBz4j//4D3zmM59BR0cH7rvvPrS0tOAnP/kJdu/ejUcffRSf+tSncN9995Xclt/vx86dO/Hoo49i/fr1+M53vlPFkRFE6ZDoE6Zj3759uPTSS+HxeAAAH/rQh/D8889nnMPzPL7zne9gz549+Na3voWf//zniEQiJbfR3d1tmGZde+21eOGFFyo3AIJYBiT6hOlQVTXjZ8YYZFnOOBaJRHD99ddjfHwcb3rTm7Bz586y2siulZD+M0GsJCT6hGkQRRGyLOOiiy7CH//4R8PD/Kc//Sne/OY3A9CqeCmKgmPHjoHjONx4441485vfjN/97ndQFKXktkZHR/H6668D0Pzj3/rWt1Z+QASxBEj0CdPg9Xqxdu1afOMb38ANN9yAnTt34oorrsD8/DxuvvlmAMA73vEOfOYzn4Hb7ca5556L97znPbjyyivh8Xhw+vTpktvasGEDvvOd7+Dqq69GIBDADTfcUK1hEURZkLUyQRCEiaBAI0EsgRMnTuBv//Zv8762a9cubNu2rcY9IojSoJk+QRCEiaCYPkEQhIkg0ScIgjARJPoEQRAmgkSfIAjCRPw/fC/fCifs9foAAAAASUVORK5CYII\u003d\n"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": "sns.regplot(x\u003d\"total_bill\", y\u003d\"tip\", data\u003dtips)\n#sns.lmplot(x\u003d\"total_bill\", y\u003d\"tip\", data\u003dtips)",
      "metadata": {
        "pycharm": {
          "metadata": false,
          "name": "#%%\n",
          "is_executing": false
        }
      }
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "outputs": [
        {
          "data": {
            "text/plain": "\u003cmatplotlib.axes._subplots.AxesSubplot at 0x2149087d8d0\u003e"
          },
          "metadata": {},
          "output_type": "execute_result",
          "execution_count": 11
        },
        {
          "data": {
            "text/plain": "\u003cFigure size 432x288 with 1 Axes\u003e",
            "image/png": 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\u003d\u003d\n"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": "# size和tip的会馆关系\nsns.regplot(x\u003d\"size\", y\u003d\"tip\", data\u003dtips)\n",
      "metadata": {
        "pycharm": {
          "metadata": false,
          "name": "#%%\n",
          "is_executing": false
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": "### 离散数据 类别等的回归需要加上抖动处理,更加趋于连续化\n* x/y_jitter",
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    {
      "cell_type": "code",
      "execution_count": null,
      "outputs": [],
      "source": "\n#size 为离散值2 3 4 5等 不适合做回归,\n# 要对数据加入小幅度的抖动\nsns.regplot(data\u003dtips, x\u003d\"size\", y\u003d\"tip\", x_jitter\u003d.05) # size 进行上下0.05 抖动\n",
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          "name": "#%%\n"
        }
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